4.5 Verify the OML4R Server Installation
To verify the success of an OML4R Server installation for Oracle Database 12c and earlier using the server.sh script, you can view the log files. For an Oracle Database 18c and later installation, the SQL script rqcfg.sql returns any errors encountered.
For 12c and Earlier, View Log Files
The OML4R Server installation script server.sh creates log files in the server subdirectory of the installation directory. Examine the log files to verify the success of the installation process.
The following commands on a Linux or UNIX system list the log files:
cd ./oml4r_server_install_dir/server
ls *.log
outcdb.log rqconfig.log rqdrop.log rqgrant.log rqinst.log rqpdrp.log rqproc.log rquser.log
If there are problems with the installation and you are unable to resolve them, you can request help from My Oracle Support or from the R Technologies in Data Warehousing.
Example 4-1 Run Examples to Verify the Server Installation
First execute these commands from an R instance directly on the database server and then execute them from the OML4R client.
Start R using the ORE script and load the ORE library.
% ORE
> library(ORE)
Connect to the server. This example connects as the user OML_USER.
ore.connect("OML_USER", password="OML_USERpsw", conn_string="", all=TRUE)
Execute some functions.
## Is the OML4R client connected to the OML4R server?
## The output of this function should be TRUE.
ore.is.connected()
## List the available database tables.
ore.ls()
## Push an R dataframe to a database table.
df <- data.frame(a="abc",
b=1.456,
c=TRUE,
d=as.integer(1))
of <- ore.push(df)
## Run the self-contained example code in the help files associated with
## the following functions.
example("ore.glm") ## Builds an OML4R generalized linear model.
example("ore.stepwise") ## Builds an OML4R stepwise least squares regression model.
example("ore.odmAI") ## Builds an OML4SQL attribute importance model.
example("ore.doEval") ## Runs an embedded R execution function.Related Topics
Parent topic: Install Oracle Machine Learning for R Server